Tag Archives: medical writing

how everyone sees SEO differently

Search Engine Optimization for Medical Writers

Demystifying Search Engine Optimization for Medical Writers

Mystery surrounds search engine optimization (SEO).  What is the best way to accomplish SEO?  Pay for Google ads?  Find a shady, off-shore company to click on your site 100,000 times?  Use a consultant?  Plunge into the SEO world on your own? This article will assist content generators to better understand and assist clients with SEO.  As communicators of information, medical writers need to be aware of what works and what does not in writing for the purpose of search engine optimization (SEO).

Having good solid information for readers in the best SEO.

Google considers pages that have “an impact on your current or future well being [sic]” Your Money or Your Life (YMYL) pages.  YMYL pages contain content deemed by Google to have a big impact on your life—obviously medical information falls into this category.  YMYL pages are scrutinized more than non-YMYL pages.

To understand SEO, it is necessary to understand how a Google search works.  Google keeps its computer program, or algorithm for searching the web, a deep, dark secret.   That’s fine; I am not qualified to dissect lines of code—let’s go with broader concepts.  Google web crawls, or looks through the content of web pages.  Web crawling is done automatically, any time of the day or night.  The website needs to be available at all times (no down servers), to keep the web crawlers (called spiders) happy.

how everyone sees search engine optimization differently

SEO humor:  how everyone sees it differently

White Hat vs. Black Hat Methods

As with many things, there are “white hat” and “black hat” methods.  White methods are good, upfront means to build SEO.  Black hat methods are seen as devious and underhanded.   The preferable way to optimize your ranking in a search engine is to develop good, useful content. This is white hat SEO, versus trying to fool the search engines, the black hat approach.

Black Hat Methods to Avoid

Some of the black hat techniques that have been tried in the past include key word stuffing:  simply adding keywords about 200 times to the bottom of a webpage. This worked until Google nixed it.   Then there were hidden key words, adding hundreds of keywords on a webpage in white on a white background—you can’t see it, but Google’s spiders can.  This worked until Google nixed it.  Next there were link farms, websites set up just to link to your page. This worked until Google nixed it.  (See a pattern developing here?)

Having all kinds of slick tricks may work for a while, until Google realizes what you are up to, which will then lead to your ranking sinking like a rock.  Google can and will penalize your site in the rankings if you violate their best practices guidelines.  Or they could even remove you from their indexing service. 

illustration of white hat and black hat

Different methods of SEO

Black hat methods are eventually figured out by Google.  Once Google finds out what you are up to, not only will the ruses fail to work, you will be punished.  Try to stick to the white hat method of SEO—develop good content that is useful to readers.

Details on White Hat Methods for Search Engine Optimization

1.  Select appropriate key words. This can be trickier than it sounds.  Brainstorming with the client is a good place to begin.  If you are working on a project, be sure to visit Google Ad Words. I believe you can still set up an account without having to purchase anything.  Use the tools to search various words that your target audience may use to find your client.  Google will give you data on how often these words are searched on Google’s search engine.

2.  Find a good online text editor with SEO optimization tools. Online webpage text editors such as Word Press offer free SEO plug-ins like SEO powered by Yoast. Or if you are writing a scientific paper, ensure that keywords are in the title, abstract, and throughout the article; a simple keyword search in your favorite text editor will suffice.

3.  Make your file names keyword rich. Include keywords in all file names, whether for text, images, or other media.  Title display in search engines is limited to first 70 characters, so keep your headlines brief and keyword rich.  Your URL (Uniform Resource Locator or website address) also needs to have keywords.  Don’t go overboard with keywords—limit yourself to 5 to 7—or Google will move you down in the rankings.

4.  Ensure a minimum of 300 words per page. If there are more than 700 words, reader frequently stop reading because it takes too long. Google gets bored with long pages, too.  Longer posts should be broken up into several pages.  Rather than present readers with a wall of text, remember to use quotation pull out, subheadings, and graphics.  [Side bar:  This article is longer than 1000 words, but since it is targeting medical writers, for whom plowing through several 5000-word papers are all in day’s work, that is fine.]

5.  Link to other sites with excellent content. I am thrilled that the Mayo Clinic webpages have such a high ranking in the Google search engine.  They are a known, trusted name in medical matters, so be sure to look for content on their site (to which you can link) that may also be of interest to your readers.  When reading on the Mayo Clinic website, I never find myself thinking “this was going along swimmingly, but now the writer is on Planet Crazy.”

 

 

Several of the words used in this article: Google, content, keyword, medical writer, search engine optimization

Word Cloud based on this article.

6.  Write with the end goal in mind: Are you publicizing your group, promoting your personal brand, or working for a client to sell products? Information is key. Be sure to inform your readers about good information they need.  Normally, medical writer generate informational web content for a general audience, like those visiting websites.  Use simpler sentence construction and terminology.

7.  Add images and other media. Good pictures, graphics, charts, cartoons, and even videos added to your pages will improve search engines rankings.  While you want the images to display with enough resolution to avoid pixelation, do not make them huge—300 by 300 pixels is a good ballpark for image size.  Smaller or larger sizes may work, too.  Remember to use keywords in the meta-information for your pictures and other media.  Get permission to use images or pay licensing fees for stock images.

8.  Content is king. Write content well, with useful information for your audience. Produce high quality content and remove any low-quality content.  Encourage sharing and commenting on your content.

9.  Maintain your website. Regularly check for broken links and either fix or remove them. Google is serious: they threatened to de-list a non-profit website for not fixing a hacking that took advantage of their excellent SEO.  Keep in mind a major redesign of a website may drastically change your search ranking.

10.  Are your SEO efforts paying off? Check your rankings once a month to show concrete measures of how well your SEO efforts are paying off.

DeeAnn Visk, PhD, is a freelance medical writer and editor. Pharmacogenetics, high throughput screening, cell culture, molecular biology, and in vitro diagnostics are her areas of expertise. DeeAnn lives in the San Diego area with her husband, kids, and two spoiled hens. You are welcome to contact her at deeannlwv@gmail.com.

© 2018 DeeAnn Visk. All rights reserved

 

Metabolomics integrates the effects of the environment with the effects of genetics

Metabolomics and Precision Medicine

Advancing Precision Medicine: Genomics, Metabolomics, and Clinical Trials

Monday, October 12 was the evening of an interesting talk at BIOCOM. Teresa Gallagher, founder of the San Diego Clinical Research Network (SDCRN) introduced the moderator of the event, Arnold Gelb, MD, Senior Medical Director at Halozyme. Rather than attempt to summarize all of the topics examined, the goal of this blog is to give a sampling of some of the areas discussed during the event.

Deterministic versus probabilistic genetics

The first speaker of the evening was Amalio Telenti, MD, PhD, Head of Genomics at Human Longevity, Inc. His talk touched on the ever-present nature vs. nurture debate. Do our genes determine a particular characteristic or merely influence the probability of developing that characteristic? In the world of whole genome sequencing, this can be described as deterministic versus probabilistic genetics.

In general, a deterministic trait would be something like Tay-Sachs Disease: if you have two copies of the gene for this condition, you have a better than 99% chance of developing the disease. A probabilistic trait is one with many genes that influence it, like height. Outside factors like disease and diet also affect how tall an individual grows. Hence, height is a probabilistic trait.

Telenti predicted that genomics will not revolutionize all aspects of medicine; but some medicine will be revolutionized profoundly; clinical trials will benefit the most. Genomics will be employed to stratify patient populations both before studies are commenced and after all the data is collected. Ideally genomics will be utilized to both determine who benefits from a drug and who should not take the drug.

Metabolomics combines genetics and environment

Steve Watkins, PhD, Chief Technology Officer of Metabolon spoke next.  Metabolon specializes in metabolomics, offering comprehensive measurements of small molecules such as glucose, cholesterol, cortisol, and amino acids in a CLIA-certified lab.

Metabolites reflect the integration of genetic and environmental influences on an individual.  Diseases can be prevented and diagnosed by checking on an individual’s metabolites. Response to disease treatment can be monitored by testing metabolites. Metabolomics is emerging as an effective tool in precision medicine.

Metabolomics integrates the effects of the environment with the effects of genetics

A person’s genome and environment affect their metabolome. Used with permission from Metabolon.

Watkins shared that Proceedings of the National Academy of Sciences recently published a study led by Baylor University’s Tom Caskey, MD. Caskey comprehensively tested the metbolites of many patients with no frank disease.  Metabolon’s platform spotted underlying health issues not previously noticed in the patients’ genetic data.

For example, Patient 3905 had very high levels of sorbitol and fructose, but no clinically significant mutation was reported in their genome.  Looking back at the genomic data for that individual, a mutation in the fructose pathway indicating “fructose intolerance” was discovered. This mutation had been overlooked previously. When discussing these results with the patient, the patient simply stated that fruit bothered him, so he refrained from eating it.

In the same study, Patient 3923 carried a gene for Xanthinuria type 1.  He showed no symptoms of the disease such as kidney stones, suggesting the gene was not penetrant (or not expressed), leaving the patient symptom-free.

In conclusion, Watkins stated that metabolomics can be used in a number of ways:

1)  By identifying pathways of interest for genetic assessment

2)  By revealing non-penetrance of genes suspected of being deleterious

3)  By enabling monitoring and understanding of metabolic conditions

Which drugs to use in cancer treatment?

The final speaker for the evening was Nicholas Schork, PhD Professor and Director of Human Biology at the J. Craig Venter Institute. He focused on emerging themes of design for precision medicine trials.

Schork presented several novel ideas. One was the idea of vetting algorithms for the treatment of cancers based on the mutations the cancers carry. Some hospitals already use this method, begging the question of who has the best algorithm for cancer treatment. As Schork points out, this has led to some interesting conversations with the FDA. He envisions clinical trials in the future for the evaluation of algorithms for cancer treatment with existing drugs, in direct contrast to the conventional clinical trial, usually designed to assess the effectiveness of a new drug.

In all, this was an exciting presentation of cutting-edge research and future directions in precision medicine.

Yes, these are lipids. But there are so many more inside your body; and they do more than store fat!

Annual Lipids Meeting in La Jolla California

The 2015 meeting on Lipids—focusing on their impact in cancer, metabolic, and inflammatory diseases—took place on Tuesday and Wednesday, May 12 and 13 at the Scripps Seaside Forum at UCSD’s Scripps Institute of Oceanography (SIO). With a beautiful venue and superb facilities, what more can you ask for? How about some really interesting science.

Lipids are generally thought of as fats. But in a biological system, they are much more. They include chemokines and other signaling molecules involved in signal transduction to and from the cell membrane. Metabolically, lipids also play an important role. Innovation in technology allow the study all the lipids in an organism (yeast, bacteria, or animal), leading to a new field of study: lipidomics. Once again, UCSD is on the cutting edge, with an established program and website in the field.

ocean, palm trees, La Jolla pennisula, green lawn with white chairs; breakfast view for lipids conference

View from the Scripps Seaside Forum at UCSD’s Scripps Institution of Oceanography.

Michael Snyder, the keynote speaker, has subjected himself to a battery of “omic” studies including his personal genome, exosome, microbiome, epigenome, proteome, metabolome, transcriptome, auto-antibody-ome, as well as cytokines. Data from these samples comprise the “Snyerdome”. All this was done in the interest of personalized medicine. These studies were done not only at one time point, but over a range of times, making it longitudinal.

Mike sees the data providing insights into how to managing healthcare in healthy individuals to predict risk, diagnose, monitor, and treat the patient, in this case, himself.

“He has also combined different state-of–the-art “omics” technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine” (from lab website).

in the future, Mike sees genomes being sequenced before birth and all this information being channeled through your smart phone. Patients will also bear more responsibility for maintaining their health with all the information they have; they will need to learn to maintain a balanced life.

The next speaker, David Wishart, discussed how to link lipidomics to laboratory medicine. He noted that in the rationalization of translating basic research to something of value in the clinic, researcher often cite the possibility of developing a new:          

  • surgical technique
  • invent a new medical device
  • drug
  • drug target
  • medically important gene
  • biomarker

All these are good outcomes; some are more likely than others. Practitioners of lipidomics are most likely to have the best luck in developing new biomarkers; not many are surgeon and drug development has about a 0.001% success rate from basic science to the prescription bottle.

lipids, lipids, lipids

Slide from David Wishart’s talk listing the number of FDA approved clinical tests from omic data

Discovery of new biomarkers is a realm where omics, specifically lipidomics, will meet a great chance of success. For this comparison, David recommends using the statistical ROC test, which is routinely used to evaluate medical test. This test gives a good sense of a medical test’s specificity and sensitivity by plotting the true positive rate over the false positive rate.

Example of ROC curve with an assessment of the area under the curve. The PSA referred to here is the amount of Prostate-Specific Antigen test; phi refers to a different, more specific calculation with less false positives than the PSA test alone.

 

ROC curve used to show predictive value of a test for prostate cancer using two different methods.

ROC curve used to show predictive value of a test for prostate cancer using two different methods.

Or you can just know that an ROC of 0.5 is worthless, while 1.0 is perfect.

Thus, from the graph above, using the PSA test alone to determine the risk of prostate cancer is poor. A better method is to use the phi method.

Work done looking at 3 to 5 biomarkers can have great ROC results. For example, predicting congenital heart defects by looking at the level of 3 carotenes, yields a ROC of 0.98. Other areas of success with high ROC scores include endometrial cancer, prostate cancer, and chronic fatigue syndrome.

David urged participants to become more quantitative to move their research into the clinic; using the website www.roccet.ca to generate ROC curves for your data is a great place to begin.

The numerous other speakers all gave fantastic talks.

In this smaller conference, I was able to browse through the all posters, read all the titles and talk to the presenters. Large conventions tend to lack the sense of intimacy and fraternity found in this lipidomics meeting. Kudos to the organizers for a successful event. A convivial group, I would highly recommend this meeting.

 

Optimizing 2D Assay Kits for Use on 3D Cultures

Assay Optimization for 3D Cultures

No, not this 3D culture

3D Movie Culture

The culture of 3D movie goers.

But this kind

3D Cultures in a tissue culture context

Tissue culture cells growing in three dimensions.

Traditionally, mammalian cell culture means living cells grown outside the body on specially treated tissue culture plates in specialized incubators. Millions (dare I say billions?) of experiments utilizing this technique leading to huge advances in research and medicine.

To improve on this convention, innovators develop cultures that grow, not just in two dimensions (2D), but three dimensions (3D). General consensus in the field now is that these 3D cultures are more physiologically relevant—closer to native whole organisms—than conventional 2D cultures.

With more use of 3D cultures in business and research, a new challenge to testing larger volumes of cells arises. Almost all previous assays used to test qualities of cells in culture have only been tested and optimized for traditional 2D models. What hurdles face scientist who want to test (assay) their cultures in 3D rather than 2D?

Terry Riss, a Promega scientist, presented his company’s findings in a talk at the Society of Toxicologist meeting on Monday, March 23, 2015 at the San Diego Convention Center. Promega’s work has been primarily conducted with spheroids generated with a hanging drop method from a company aptly named In Sphero

In thinking about differences between 2D and 3D cultures, one huge differences is the ability of reagents to diffuse longer distances into cells. Two dimensional cells tend to grow flat and spread out on the dish surface, allowing great accessibility to the innards of the cells, which scientist are obsessively interested in.

Promega offers various “Glo” assays for cell viability. Generally they are better than the usual MTT or resazurin tests in that they are less toxic to the cells and permit the same cells to be used again after the assays for even more assays (there we go again, us scientist and our obsession with assays).

In general, Riss advocates optimizing any off-the-shelf assays developed for 2D cell culture with your own particular cell line and application (basic good lab practice in my book!).

Try increasing these three parameters:

  • Detergent concentration to lysis the cells
  • Physical disruption used to dissociate the cells
  • Time of incubation

As always, remember to  include controls, both positive and negative, and optimize the experiment to your particular assay needs.

As the drug development moves towards more 3D cell culture models, the need for assays of these cultures will grow.  Promega is adding to their repertoire of kits to meet this need.